Closed-loop automatic defect inspection and classification

ABSTRACT

Inspection apparatus includes an imaging module, which is configured to capture images of defects at different, respective locations on a sample. A processor is coupled to process the images so as to automatically assign respective classifications to the defects, and to autonomously control the imaging module to continue capturing the images responsively to the assigned classifications.

FIELD OF THE INVENTION

The present invention relates generally to automated inspection, andspecifically to methods and systems for detection and analysis ofmanufacturing defects.

BACKGROUND OF THE INVENTION

Automatic Defect Classification (ADC) techniques are widely used ininspection and measurement of defects on patterned wafers in thesemiconductor industry. The object of these techniques is not only todetect the existence of defects, but to classify them automatically bytype, in order to provide more detailed feedback on the productionprocess and reduce the load on human inspectors. ADC is used, forexample, to distinguish among types of defects arising from particulatecontaminants on the wafer surface and defects associated withirregularities in the microcircuit pattern itself, and may also identifyspecific types of particles and irregularities.

Various methods for ADC have been described in the patent literature.For example, U.S. Pat. No. 6,256,093 describes a system for on-the-flyADC in a scanned wafer. A light source illuminates the scanned wafer soas to generate an illuminating spot on the wafer. Light scattered fromthe spot is sensed by at least two spaced-apart detectors, and isanalyzed so as to detect defects in the wafer and classify the defectsinto distinct defect types.

As another example, U.S. Pat. No. 6,922,482 describes a method andapparatus for automatically classifying a defect on the surface of asemiconductor wafer into one of a number of core classes, using a coreclassifier employing boundary and topographical information. The defectis then further classified into a subclass using a specific adaptiveclassifier that is associated with the core class and trained toclassify defects from only a limited number of related core classes.Defects that cannot be classified by the core classifier or the specificadaptive classifiers are classified by a full classifier.

SUMMARY

Embodiments of the present invention that are described hereinbelowprovide improved methods, systems and software for automated inspectionand classification of defects.

There is therefore provided, in accordance with an embodiment of thepresent invention, inspection apparatus, including an imaging module,which is configured to capture images of defects at different,respective locations on a sample. A processor is coupled to process theimages so as to automatically assign respective classifications to thedefects, and to autonomously control the imaging module to continuecapturing the images responsively to the assigned classifications.

In some embodiments, the processor is configured to instruct the imagingmodule, after assigning the classifications to a first set of thedefects appearing in the images captured by the imaging module, tocapture further images of a second set of the defects responsively to adistribution of the classifications of the defects in the first set. Theprocessor may be configured to count respective numbers of the defectsbelonging to one or more of the classifications, and to instruct theimaging module to continue capturing the further images until at leastone of the numbers satisfies a predefined criterion. Typically, theprocessor is configured to cause the imaging module to continuecapturing the further images until a number of the defects belonging toa given classification reaches a predefined threshold, and then toterminate inspection of the sample.

In one embodiment, the apparatus includes a user interface, wherein theprocessor is coupled to process the images and control the imagingmodule in response to instructions received from a user via the userinterface. Additionally or alternatively, the processor is coupled toprocess the images and control the imaging module in response toinstructions received from a server via a network.

In some embodiments, the processor is configured to identify one or moreof the defects for further analysis using a different inspectionmodality. The imaging module may include multiple detectors, includingat least first and second detectors configured to capture images inaccordance with different, respective modalities, and the processor maybe configured to identify the one or more of the defects by processingfirst images captured by the first detector and to instruct the imagingmodule to capture second images of the one or more of the defects usingthe second detector. In a disclosed embodiment, the processor isconfigured to identify the one or more of the defects, based on thefirst images, as belonging to a specified class, and to choose thesecond detector for capturing the second images depending on thespecified class. The multiple detectors may be selected from a group ofdetectors consisting of electron detectors, X-ray detectors, and opticaldetectors.

In one embodiment, the apparatus includes a memory, which is configuredto store definitions of a plurality of defect classes in terms ofrespective classification rules in a multi-dimensional feature space,and the processor is configured to extract features of the defects fromthe images, and to assign the respective classifications by applying theclassification rules to the extracted features.

In a disclosed embodiment, the imaging module includes a scanningelectron microscope (SEM), and the sample includes a semiconductorwafer.

There is also provided, in accordance with an embodiment of the presentinvention, a method for inspection, which includes capturing images ofdefects at different, respective locations on a sample using an imagingmodule. The images are automatically processed so as to assignrespective classifications to the defects, and autonomously controllingthe imaging module to continue capturing the images responsively to theassigned classifications.

The present invention will be more fully understood from the followingdetailed description of the embodiments thereof, taken together with thedrawings in which:

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic, pictorial illustration of a defect inspection andclassification system, in accordance with an embodiment of the presentinvention; and

FIG. 2 is a flow chart that schematically illustrates a method forclosed-loop defect inspection and classification, in accordance with anembodiment of the present invention.

DETAILED DESCRIPTION OF EMBODIMENTS

In common models of ADC, the image capture and image analysis functionsare separate: An imaging system, such as a scanning electron microscope(SEM), will first capture a certain number of images of differentlocations on a sample, such as a semiconductor wafer, and these imageswill then be passed to an ADC system for post hoc analysis. Because itis difficult to predict a priori how many defects of any given type willbe found on a given wafer, the post hoc approach will sometimes yieldfar more images than are needed for effective process analysis. At othertimes, the SEM will yield insufficient information, so that the waferwill have to be rescanned. Such additional scans may be aimed atproviding additional images or information that is specific for theclass of a particular defect or group of defects. For example,particle-type defects will be analyzed by energy-dispersive X-ray (EDX)analysis, and images of electrical short circuits may be acquired intilt mode, at a different angle of imaging. The above sorts ofsituations result in wasted time and testing resources.

Embodiments of the present invention address these problems byintegrating ADC capability with an imaging module, such as a SEM, anoptical inspection unit, or any other suitable type of inspectiondevice. An ADC processor analyzes each image that the imaging moduleproduces in order to classify the defects and determine whether they are“interesting,” according to user-defined criteria. For example, a givenuser may decide that he is interested only in certain defect classes,such as particle-type defects, and wishes to inspect fifty such defectsper wafer. In this case, the imaging module may scan differentlocations, randomly selected, on each wafer until it has captured imagesof fifty particle-type defects. Classifying the defect type that hasbeen captured by the imaging module at each location is done online byintegrating ADC capability with the imaging module. In the presentexample, the imaging module will output fifty images of particle-typedefects from each wafer and will then move on to process the next wafer.

In other embodiments, when the imaging module supports multipleinspection modalities, the integrated ADC processor may classify defectsaccording to defect type, and may then instruct the imaging module toapply different additional inspection operations to the different types.This sort of iterative inspection loop can be used multiple times at anygiven defect location, depending on classification results. For example:

-   -   The ADC processor may classify a given defect as particle by        analyzing a SEM image of the defect, and then, based on this        classification, instruct the imaging module to perform EDX        analysis at the same wafer location. It may then, based on the        EDX analysis, perform a second classification step using the new        information acquired from the EDX.    -   The ADC processor may classify a defect as an electrical short        by SEM image analysis, and may then, based on this        classification, instruct the imaging module to acquire a tilt        image. The ADC processor may then perform a second        classification step using the new information acquired from the        tilt image.    -   When the ADC processor initially classifies a defect as        belonging to an unknown defect class, it may instruct the        imaging module to perform optical image acquisition, and may        then, based on analysis of the optical image, perform a second        classification of the defect using the new information acquired        from the optical image.

These procedures reduce the time needed for analyzing some of the defectclasses by performing necessary analysis and re-imaging based onclose-loop classification. All the necessary information may be gatheredin one scan over the defect locations, rather than having to scan thewafer multiple times in multiple different modalities.

Thus, embodiments of this invention enable an imaging module, such as aSEM, to use its resources more efficiently: Integration of ADCcapability with the imaging module allows it to capture prescribednumbers of images of particular types of defects and to stop capturingimages once it has met its quota, for example, rather than wasting timeon superfluous images. Additionally or alternatively, as explainedabove, classification results of defects captured initially on a givenwafer can be used to automatically guide the imaging module to capturesubsequent images in particular locations using particular modalitiesand settings. Based on initial ADC results, the imaging module can beguided to perform further analysis at specific wafer locations usinganother inspection modality, such as energy-dispersive X-ray (EDX)analysis or other forms of material analysis and different imageacquisition modes, including optical modalities, as well as inspectionby a human operator.

FIG. 1 is a schematic, pictorial illustration of a system 20 forautomated defect inspection and classification, in accordance with anembodiment of the present invention. A sample, such as a patternedsemiconductor wafer 22, is inserted into an imaging module 24. Thismodule typically comprises one or more detectors 36, for example, ascanning electron microscope (SEM) or an optical inspection device orany other suitable sort of inspection apparatus that is known in theart. Module 24 typically scans the surface of wafer 22, senses andcaptures image data, and outputs images of defects on the wafer. Theterm “images” is used broadly in the context of the present patentapplication and in the claims to refer to data regarding any sort oflocal features that can be associated with a given defect location. Suchfeatures may include, for example, the size, shape, scatteringintensity, directionality, and/or spectral qualities, as well as anyother suitable features that are known in the art.

A processor 26 receives and processes the images that are output byimaging module 24. Processor 26 comprises a classifier module 30, whichprocesses the images to extract relevant inspection feature values fromthe images of wafer 22. Typically, module 30 assigns the defects torespective classes by applying classification rules, which are stored ina memory 28, to the feature values. Classifier module 30 passes thedefect classifications to a control module 32, which controls theongoing image capture by inspection module accordingly, as described ingreater detail hereinbelow. These operations of processor 26 aretypically carried out autonomously, i.e., without operator interventionduring the inspection process, based on predefined criteria andinstructions.

Processor 26 typically comprises a general-purpose computer processor ora group of such processors, which may be integrated inside the enclosureof imaging module 24 or coupled to it by a suitable communication link.The processor uses memory 28 to hold defect information andclassification rules. Processor 26 is coupled to a user interface 34,through which a user, such as an operator of system 20, is able todefine operating criteria to be applied in processing images andcontrolling imaging module 24. Processor 26 is programmed in software tocarry out the functions that are described herein, including thefunctions of classifier module 30 and control module 32. The softwaremay be downloaded to the processor in electronic form, over a network,for example, or it may, alternatively or additionally, be stored intangible, non-transitory storage media, such as optical, magnetic, orelectronic memory media (which may be comprised in memory 28, as well).Alternatively or additionally, at least some of the functions ofprocessor 26 may be implemented in dedicated or programmable hardwarelogic.

Classifier module 30 may apply any suitable sort of ADC algorithm thatis known in the art to the defect image data. In one embodiment, forexample, module 30 runs multiple classifiers, including bothsingle-class and multi-class classifiers. These classifiers useclassification rules specified in a multi-dimensional feature space,which define the defect classes in terms of respective regions in thefeature space. Module 30 extracts features of the defects from theimages provided by module 24, and assigns the defect classifications byapplying the classification rules to the extracted features. Themulti-class classifier sorts the defects on this basis among a set ofpredefined defect classes (such as particle defects, pattern defects,etc.); while the single-class classifiers are defined respectively foreach class and classify defects as being within or outside the classboundaries. Classifiers of this sort are described in detail, forexample, in U.S. patent application Ser. No. 12/844,724, filed Jul. 27,2010, whose disclosure is incorporated herein by reference.

Processor 26 typically communicates, via a network, for example, with anADC server 38. The server provides processor 26 with “recipes” fordefect analysis and classification and may update these recipes fromtime to time. Processor 26 reports defect inspection and classificationresults to the server. Because classification is performed locally atimaging module 24, the volume of data that must be communicated toserver 38 is greatly reduced, relative to systems in which raw imagesare transmitted to the ADC server. Additionally or alternatively,processor 26 may convey some image data and/or intermediateclassification results to server 38 for further processing.

FIG. 2 is a flow chart that schematically illustrates a method forclosed-loop defect inspection and classification, in accordance with anembodiment of the present invention. The method is described here, forthe sake of convenience and clarity, with reference to the specificarchitecture of system 20, but it may similarly be implemented in otherinspection systems with integrated defect classification capabilities.The method, as described below, takes advantage of the availability ofmultiple detectors 36 and imaging modalities in imaging module 24,although aspects of the method may also be applied in single-modalitysystems.

Control module 32 receives defect capture instructions, at aninstruction input step 40. These instructions may be programmed by auser, such as an operator of system 20, via user interface 34, forexample, or they may alternatively be downloaded to the system via anetwork or conveyed to the system by any other suitable means.Typically, the instructions define one or more criteria applying to thedistribution of defects that system 20 is to seek. For example, theinstructions may specify one or more classes of defects and the numberof defects in each such class that the system should attempt to find oneach wafer. The instructions may also specify a timeout conditionindicating, for example, that inspection of a wafer should terminateafter capturing images of some maximal number of possible defect siteswithout reaching the target defect distribution. Additionally oralternatively, the instructions may specify additional image acquisitionmodes to be applied to one or more classes of defects, based onclassification decisions made at each iteration of a closed-loopinspection process. Such image acquisition modes may include EDX, tiltimaging, optical imaging, and any other information that can becollected by detectors 36 during the scan.

Control module 32 instructs imaging module 24 to capture an image of adefect on wafer 22, at an image capture step 42. Module 24 passes theimage (and/or extracted features of the image) to classifier module 30,at a defect classification step 44. Module 30 applies the appropriaterules to the defect features in order to assign the defect to aparticular class. Module 30 may optionally tag the defect for furtheranalysis, such as when the defect cannot be classified with confidenceusing the rules in memory 28 or when the instructions provided at step40 instruct processor 26 that certain types of defects should be soidentified. Depending on these instructions, these tagged defects may beprocessed further using another imaging modality in imaging module 24.Alternatively or additionally, certain tagged defects may be passed to ahuman inspector and/or to another inspection machine, such as an X-rayanalysis tool.

Control module 32 receives and records the classification of each defectfrom classifier module 30, at a distribution checking step 46. At thisstep, if the defect is of a type that has been tagged for furtherimaging and classification, module 32 may return to step 42 and instructimaging module 24 to capture another image of the same defect usinganother specified modality (such as EDX, tilt, or optical imaging, asexplained above). Additionally or alternatively, module may, at step 46,compare the distribution of defects classified so far to theinstructions that were received at step 40. If the instructions have notyet been fulfilled (or timed out), the control module returns to step 42and instructs imaging module 24 to capture a defect image at anotherlocation.

If at step 46 the instructions have been fulfilled (by having performedall specified imaging and classification steps and having collected therequired number of images of defects belonging to a specified class orclasses, for example) or timed out, module 32 terminates the inspectionof wafer 22 and issues a report, at an inspection completion step 48.The report may include images of the defects in the class or classesspecified by the instructions, or it may simply contain tabulated defectdata with respect to wafer 22. System 20 may then proceed to inspectionof the next wafer.

Although the above method is described, for the sake of clarity, in thespecific context of defect classification in system 20 and imagingmodule 24, these methods may similarly be applied in other systems andapplications of automated inspection, and are in no way limited tosemiconductor wafer defects or to SEM images. It will thus beappreciated that the embodiments described above are cited by way ofexample, and that the present invention is not limited to what has beenparticularly shown and described hereinabove. Rather, the scope of thepresent invention includes both combinations and sub-combinations of thevarious features described hereinabove, as well as variations andmodifications thereof which would occur to persons skilled in the artupon reading the foregoing description and which are not disclosed inthe prior art.

1. Inspection apparatus, comprising: an imaging module, which isconfigured to capture images of defects at different, respectivelocations on a sample; a memory, which is configured to storedefinitions of a plurality of defect classes in terms of respectiveclassification rules in a multi-dimensional feature space; and aprocessor, which is coupled to the imaging module and the memory and isconfigured to process the images so as to automatically extract featuresof the defects from the images and to assign respective classificationsto the defects by applying the classification rules to the extractedfeatures, and to autonomously control the imaging module to continuecapturing the images responsively to the assigned classifications. 2.The apparatus according to claim 1, wherein the processor is configuredto instruct the imaging module, after assigning the classifications to afirst set of the defects appearing in the images captured by the imagingmodule, to capture further images of a second set of the defectsresponsively to a distribution of the classifications of the defects inthe first set.
 3. The apparatus according to claim 2, wherein theprocessor is configured to count respective numbers of the defectsbelonging to one or more of the classifications, and to instruct theimaging module to continue capturing the further images until at leastone of the numbers satisfies a predefined criterion.
 4. The apparatusaccording to claim 3, wherein the processor is configured to cause theimaging module to continue capturing the further images until a numberof the defects belonging to a given classification reaches a predefinedthreshold, and then to terminate inspection of the sample.
 5. Theapparatus according to claim 1, and comprising a user interface, whereinthe processor is configured to process the images and control theimaging module in response to instructions received from a user via theuser interface.
 6. The apparatus according to claim 1, wherein theprocessor is configured to process the images and control the imagingmodule in response to instructions received from a server via a network.7. The apparatus according to claim 1, wherein the processor isconfigured to identify one or more of the defects for further analysisusing a different inspection modality.
 8. The apparatus according toclaim 7, wherein the imaging module comprises multiple detectors,including at least first and second detectors configured to captureimages in accordance with different, respective modalities, and whereinthe processor is configured to identify the one or more of the defectsby processing first images captured by the first detector and toinstruct the imaging module to capture second images of the one or moreof the defects using the second detector.
 9. The apparatus according toclaim 8, wherein the processor is configured to identify the one or moreof the defects, based on the first images, as belonging to a specifiedclass, and to choose the second detector for capturing the second imagesdepending on the specified class.
 10. The apparatus according to claim1, wherein the imaging module comprises a scanning electron microscope(SEM).
 11. A method for inspection, comprising: storing definitions of aplurality of defect classes in terms of respective classification rulesin a multi-dimensional feature space; capturing images of defects atdifferent, respective locations on a sample using an imaging module;automatically processing the images so as to assign respectiveclassifications to the defects by extracting features of the defectsfrom the images and applying the classification rules to the extractedfeatures, and autonomously controlling the imaging module to continuecapturing the images responsively to the assigned classifications. 12.The method according to claim 11, wherein controlling the imaging modulecomprises instructing the imaging module, after assigning theclassifications to a first set of the defects appearing in the imagescaptured by the imaging module, to capture further images of a secondset of the defects responsively to a distribution of the classificationsof the defects in the first set.
 13. The method according to claim 12,wherein processing the images comprises counting respective numbers ofthe defects belonging to one or more of the classifications, and whereininstructing the imaging module comprises directing the imaging module tocontinue capturing the further images until at least one of the numberssatisfies a predefined criterion.
 14. The method according to claim 13,wherein directing the imaging module comprises causing the imagingmodule to continue capturing the further images until a number of thedefects belonging to a given classification reaches a predefinedthreshold, and then to terminate inspection of the sample.
 15. Themethod according to claim 11, and comprising receiving instructions froma user via a user interface, wherein the images are processed and theimaging module is controlled responsively to the instructions.
 16. Themethod according to claim 11, and comprising receiving instructions froma server via a network, wherein the images are processed and the imagingmodule is controlled responsively to the instructions.
 17. The methodaccording to claim 11, and comprising identifying one or more of thedefects for further analysis using a different inspection modality. 18.The method according to claim 17, wherein the imaging module comprisesmultiple detectors, including at least first and second detectorsconfigured to capture images in accordance with different, respectivemodalities, and wherein automatically processing the images comprisesidentifying the one or more of the defects by processing first imagescaptured by the first detector, and controlling the imaging modulecomprises instructing the imaging module to capture second images of theone or more of the defects using the second detector.
 19. The methodaccording to claim 18, wherein identifying the one or more of thedefects comprises classifying the one or more of the defects, based onthe first images, as belonging to a specified class, and whereininstructing the imaging module comprises choosing the second detectorfor capturing the second images depending on the specified class. 20.The method according to claim 18, wherein the multiple detectors areselected from a group of detectors consisting of electron detectors,X-ray detectors, and optical detectors.